
!pip install gym yfinance ta

Requirement already satisfied: gym in c:\users\nacho\anaconda3\lib\site-packages (0.26.2)
Requirement already satisfied: yfinance in c:\users\nacho\anaconda3\lib\site-packages (0.2.36)
Requirement already satisfied: ta in c:\users\nacho\anaconda3\lib\site-packages (0.11.0)
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Requirement already satisfied: gym-notices>=0.0.4 in c:\users\nacho\anaconda3\lib\site-packages (from gym) (0.0.8)
Requirement already satisfied: pandas>=1.3.0 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (1.5.3)
Requirement already satisfied: requests>=2.31 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (2.31.0)
Requirement already satisfied: multitasking>=0.0.7 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (0.0.11)
Requirement already satisfied: lxml>=4.9.1 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (4.9.2)
Requirement already satisfied: appdirs>=1.4.4 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (1.4.4)
Requirement already satisfied: pytz>=2022.5 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (2022.7)
Requirement already satisfied: frozendict>=2.3.4 in c:\users\nacho\anaconda3\lib\site-packages (from yfinance) (2.4.0)
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def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn
warnings.filterwarnings('ignore')

import gym
import yfinance as yf
import ta   # ta documentation: https://technical-analysis-library-in-python.readthedocs.io/en/latest/
from tqdm import tqdm
import numpy as np
import pandas as pd
from itertools import accumulate
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


sns.set_style("darkgrid")


tickers = "^GSPC"  #"^GSPC" S&P500
df = yf.download(tickers = tickers ,       # list of tickers
                  period = "2y",         # time period
                  interval = "1h",       # trading interval
                  ignore_tz = True,      # ignore timezone when aligning data from different exchanges?
                  prepost = False)       # download pre/post market hours data?  
print("shape of dataset: ",df.shape)
df.head()

[*********************100%%**********************]  1 of 1 completed

shape of dataset:  (3502, 6)


split_line = int(np.round(len(df)* 0.1)) # use 10% of data for testing


# Get min and max of 'Adj Close'
adj_close_min = df['Adj Close'].min()
adj_close_max = df['Adj Close'].max()

# Set the background color
sns.set_style(rc = {'axes.facecolor': 'lightsteelblue'})
plt.figure(figsize=(12,4))

# Create line plot
sns.lineplot(data=df["Adj Close"],label = "Adjusted Close Price")
plt.xticks(rotation=45)
plt.title(tickers)

# single vline with full ymin and ymax
plt.vlines(x=df.index[-split_line], ymin=adj_close_min, ymax=adj_close_max, colors='green', ls=':', lw=2, label='Train-Test Split Point')

plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left')
plt.show()


df['EMA7'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 7, fillna= False).ema_indicator()
df['EMA14'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 14, fillna= False).ema_indicator()
df['EMA50'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 50, fillna= False).ema_indicator()
df['EMA200'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 50, fillna= False).ema_indicator()


# Set the background color
sns.set_style(rc = {'axes.facecolor': 'lightsteelblue'})
plt.figure(figsize=(12,4))

# Create line plot
sns.lineplot(data=df["Adj Close"],label = "Adjusted Close Price")
sns.lineplot(data=df["EMA7"],label = "EMA7")
sns.lineplot(data=df["EMA14"],label = "EMA14")
sns.lineplot(data=df["EMA50"],label = "EMA50")
sns.lineplot(data=df["EMA200"],label = "EMA200")

# Rotate x-axis labels for better readability
plt.xticks(rotation=45)

# Set the title of the plot
plt.title(tickers)

Text(0.5, 1.0, '^GSPC')


df['MACD_line'] =ta.trend.MACD(close= df['Adj Close'], window_slow= 26, window_fast= 12, window_sign = 9, fillna = False).macd()
df['MACD_signal'] =ta.trend.MACD(close= df['Adj Close'], window_slow= 26, window_fast= 12, window_sign = 9, fillna = False).macd_signal()
df['MACD_diff'] =ta.trend.MACD(close= df['Adj Close'], window_slow= 26, window_fast= 12, window_sign = 9, fillna = False).macd_diff()


df['RSI']=ta.momentum.RSIIndicator(close= df['Adj Close'], window= 14, fillna= False).rsi()


# adding Volumn indicator
df['OBV']= ta.volume.OnBalanceVolumeIndicator(close= df['Adj Close'], volume= df['Volume'], fillna = False).on_balance_volume()
df.head()


df['BBH']=ta.volatility.BollingerBands(df['Adj Close'], window = 20, window_dev = 2, fillna = False).bollinger_hband_indicator()
df['BBL']=ta.volatility.BollingerBands(df['Adj Close'], window = 20, window_dev = 2, fillna = False).bollinger_lband_indicator()
df.head()


# Check the shape of the dataframe (number of rows and columns)
df.shape

(3502, 17)


# Get the column names of the dataframe
df.columns

Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'EMA7', 'EMA14',
       'EMA50', 'EMA200', 'MACD_line', 'MACD_signal', 'MACD_diff', 'RSI',
       'OBV', 'BBH', 'BBL'],
      dtype='object')


# Drop rows with missing values (NaN values)
df=df.dropna()

# Drop specified columns from the dataframe (Open, High, Low, Close, Volume)
df = df.drop(columns=['Open', 'High', 'Low', 'Close', 'Volume'],axis=1)

# Display the first few rows of the modified dataframe
df.head()


# Splitting the dataframe 'df' into training and testing datasets
# Training data contains rows from the beginning to the split line (exclusive)
train_data = df.iloc[:-split_line,:]

# Testing data contains rows from the split line to the end
test_data = df.iloc[-split_line:,:]

# Get the number of columns (features) in the training data, which represents the input shape for the model
input_shape= train_data.shape[1]

# Print the shape of the input, training, and testing data
print(input_shape)
print('Training data:',train_data.shape)
print('Testing data',test_data.shape)

12
Training data: (3103, 12)
Testing data (350, 12)


import gym
import numpy as np

class StockTradingEnv(gym.Env):
    def __init__(self, data, initial_cash=10000, commission=0.000):
        # Define the action space as a 2D vector representing the action type (buy or sell) and number of shares to trade
        self.action_space = gym.spaces.MultiDiscrete([3, 10])  # [action, number of shares]

        # Define the observation space as a X-D vector containing various indicators of the stock price
        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(12,), dtype=np.float32)

        # Store the historical stock data, initial cash balance, and commission rate as instance variables
        self.data = data
        self.initial_cash = initial_cash
        self.commission = commission
        self.end_step = len(self.data) - 1

        # Reset the environment to its initial state
        self.reset()

    def reset(self):
        # Set the current step to 0 and reset the state variables
        self.current_step = 0
        self.profits = 0
        self.shares_held = 0
        self.cash = self.initial_cash
        self.buy_price = 0
        self.sell_price = 0
        self.done = False

        # Return the initial observation
        return self._get_obs()

    def step(self, action):
        action_type, amount = action

        if action_type == 0:
            cost = self.data['Adj Close'][self.current_step] * amount
            if self.cash >= cost:
                self.cash -= cost
                self.shares_held += amount
                self.buy_price = self.data['Adj Close'][self.current_step]
        elif action_type == 1:
            shares_sold = min(self.shares_held, amount)
            revenue = self.data['Adj Close'][self.current_step] * shares_sold
            self.cash += revenue
            self.profits += (self.data['Adj Close'][self.current_step] - self.buy_price) * shares_sold
            self.shares_held -= shares_sold
            self.sell_price = self.data['Adj Close'][self.current_step]

        self.current_step += 1

        # Calculate the total portfolio value
        portfolio_value = self.cash + (self.shares_held * self.data['Adj Close'][self.current_step])
  
        # Calculate the reward based on the change in portfolio value at each stept
        reward = portfolio_value - (self.cash + (self.shares_held * self.data['Adj Close'][self.current_step - 1]))
        done = (self.current_step == self.end_step)
        return self._get_obs(), reward, done, {}


    def _get_obs(self):
    # Calculate the observation vector for the current step
        obs = np.array([
            self.data['Adj Close'][self.current_step],
            self.data['EMA7'][self.current_step],
            self.data['EMA14'][self.current_step],
            self.data['EMA50'][self.current_step],
            self.data['EMA200'][self.current_step],
            self.data['MACD_line'][self.current_step],
            self.data['MACD_signal'][self.current_step],
            self.data['MACD_diff'][self.current_step],
            self.data['RSI'][self.current_step],
            self.data['OBV'][self.current_step],
            self.data['BBH'][self.current_step],
            self.data['BBL'][self.current_step],
        ])

        # Return the observation vector
        return obs


#Import necessary libraries
import random
import numpy as np
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD, Adam

class DQNAgent:
    def __init__(self, state_size, action_space,memory_size=3000):
        # Initialize instance variables
        self.state_size = state_size
        self.action_space = action_space
        self.action_size = action_space.nvec.prod()
        self.memory = deque(maxlen=memory_size)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.01
        self.model = self._build_model()

    def _build_model(self):
        # Build the neural network model
        model = Sequential()
        model.add(Dense(64, input_dim=self.state_size))
        model.add(Dense(128, activation='relu'))
        model.add(Dense(256, activation='relu'))
        model.add(Dense(128, activation='relu'))
        model.add(Dense(self.action_size, activation='relu'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    def remember(self, state, action, reward, next_state, done):
        # Store the experience in memory
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        # Choose an action using the epsilon-greedy strategy
        if np.random.rand() <= self.epsilon:
            return (random.randrange(self.action_space.nvec[0]), random.randrange(self.action_space.nvec[1]))
        act_values = self.model.predict(state)
        return np.unravel_index(np.argmax(act_values[0]), self.action_space.nvec)

    def replay(self, batch_size):
        # Train the model using random samples from memory
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                # Update target value for non-terminal states
                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)

            # Convert the action tuple to a linear index
            action_idx = np.ravel_multi_index(action, self.action_space.nvec)

            # Update the target value for the chosen action
            target_f[0][action_idx] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)

        # Decay the exploration rate if it's above the minimum threshold
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

WARNING:tensorflow:From C:\Users\nacho\anaconda3\Lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.


# Load the stock data
data = train_data

# Create the environment
env = StockTradingEnv(data)
state_size = env.observation_space.shape[0]
action_size = env.action_space.nvec.prod()

# Create and train the agent
agent = DQNAgent(state_size, env.action_space)
episodes = 100
batch_size = 32

for e in tqdm(range(episodes)):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    total_reward = 0

    while True:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        total_reward += reward
        next_state = np.reshape(next_state, [1, state_size])

        agent.remember(state, action, reward, next_state, done)
        state = next_state

        if done:
            # print(f'Episode: {e + 1}/{episodes}, Total Reward: {total_reward}, Epsilon: {agent.epsilon:.2}')
            break

    if len(agent.memory) > batch_size:
        agent.replay(batch_size)

WARNING:tensorflow:From C:\Users\nacho\anaconda3\Lib\site-packages\keras\src\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
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WARNING:tensorflow:From C:\Users\nacho\anaconda3\Lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

WARNING:tensorflow:From C:\Users\nacho\anaconda3\Lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

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import matplotlib.pyplot as plt

# Load the test data
test_data = test_data.iloc[:,:]

# change this to 1 to get records of all transactions
verbose = 0

# Create the test environment
test_env = StockTradingEnv(test_data, initial_cash=10000)

# Test the agent
state = test_env.reset()
state = np.reshape(state, [1, state_size])
total_reward = 0

# Initialize lists to store the buy and sell prices and the corresponding time steps
buy_prices = []
buy_times = []
sell_prices = []
sell_times = []

# Initialize the list to store portfolio values and add the initial portfolio value
portfolio_values = [test_env.cash]

while True:
    action = agent.act(state)
    next_state, reward, done, _ = test_env.step(action)

    # Calculate the total portfolio value
    portfolio_value = test_env.cash + (test_env.shares_held * test_env.data['Adj Close'][test_env.current_step])
    portfolio_values.append(portfolio_value)

    # Print the action details
    action_type, shares = action
    if(verbose ==1):
        if action_type == 0:
            print(f"Buy {shares} shares at price {test_env.data['Adj Close'][test_env.current_step]}")
        elif action_type == 1:
            print(f"Sell {shares} shares at price {test_env.data['Adj Close'][test_env.current_step]}")

        # Print available cash, number of shares held, and reward
        print(f"Available cash: {test_env.cash}",f",Shares held: {test_env.shares_held}",f",Reward: {reward}")
        print("------------------------------------------------")

    total_reward += reward
    next_state = np.reshape(next_state, [1, state_size])
    state = next_state
    
    # Check if a buy action was taken and record the price and time step
    if action[0] == 0:
        buy_prices.append(test_data['Adj Close'][test_env.current_step])
        buy_times.append(test_data.index[test_env.current_step])

    # Check if a sell action was taken and record the price and time step
    elif action[0] == 1:
        sell_prices.append(test_data['Adj Close'][test_env.current_step])
        sell_times.append(test_data.index[test_env.current_step])

    if done:
        print(f'Test: Total Reward: {total_reward}')
        break

# Calculate the percentage change in the portfolio value
portfolio_percentage_changes = [((value - portfolio_values[0]) / portfolio_values[0]) * 100 for value in portfolio_values]

# Create the figure with two subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12), sharex=True)

# Plot the historical price data as a line chart on the first subplot
ax1.plot(test_data.index, test_data['Adj Close'])

# Plot the buy and sell actions as red and blue dots, respectively, on the first subplot
ax1.scatter(buy_times, buy_prices, color='b', marker='o', label='Buy')
ax1.scatter(sell_times, sell_prices, color='r', marker='o', label='Sell')

# Set the first subplot's title, x-label, and y-label
ax1.set_title('Trading History')
ax1.set_xlabel('Time')
ax1.set_ylabel('Price')
ax1.legend()

# Plot the percentage change in the portfolio value on the second subplot
ax2.plot(test_data.index, portfolio_percentage_changes, color='g', linestyle='--', label='Portfolio % Change')

# Set the second subplot's title, x-label, and y-label
ax2.set_title('Portfolio Value Change')
ax2.set_xlabel('Time')
ax2.set_ylabel('% Change')
ax2.legend()

# Display the chart
plt.show()

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Test: Total Reward: 230.52001953125

agent.epsilon = 0


# Load the test data
test_data = test_data

# Create the test environment
test_env = StockTradingEnv(test_data,initial_cash =100000)

# Define the number of Monte Carlo runs
monte_carlo_runs = 200
rewards = []

for run in tqdm(range(monte_carlo_runs)):
    # Test the agent
    state = test_env.reset()
    state = np.reshape(state, [1, state_size])
    total_reward = 0

    while True:
        action = agent.act(state)
        next_state, reward, done, _ = test_env.step(action)
        total_reward += reward
        next_state = np.reshape(next_state, [1, state_size])
        state = next_state

        if done:
            # print(f'Test Run: {run + 1}/{monte_carlo_runs}, Total Reward: {total_reward}')
            rewards.append(total_reward)
            break

# Calculate and print the average reward
average_reward = np.mean(rewards)
print(f'Average Reward after {monte_carlo_runs} runs: {average_reward}')

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# getting more data
tickers = ["GOOG","IBM","AAPL","META","AMZN"]  #"^GSPC" S&P500
df = yf.download(tickers = tickers ,       # list of tickers
                  period = "2y",         # time period
                  interval = "1h",       # trading interval
                  ignore_tz = True,      # ignore timezone when aligning data from different exchanges?
                  prepost = False)       # download pre/post market hours data?  
print("shape of dataset: ", df.shape)
df.head()

[*********************100%%**********************]  5 of 5 completed

shape of dataset:  (3502, 30)


import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

split_line = int(np.round(len(df) * 0.1))  # use 10% of data for testing

# Get min and max of 'Adj Close'
adj_close_min = df['Adj Close'].min().min()
adj_close_max = df['Adj Close'].max().max()

# Set the background color
sns.set_style(rc={'axes.facecolor': 'lightsteelblue'})
plt.figure(figsize=(12, 4))

# Loop through each stock in the 'Adj Close' column and plot
for stock in df['Adj Close'].columns:
    sns.lineplot(data=df['Adj Close'][stock], label=f'Adjusted Close Price - {stock}')

plt.xticks(rotation=45)
plt.title(', '.join(df['Adj Close'].columns))

# single vline with full ymin and ymax
plt.vlines(x=df.index[-split_line], ymin=adj_close_min, ymax=adj_close_max, colors='green', ls=':', lw=2, label='Train-Test Split Point')

plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left')
plt.show()


def add_indicators(df):

    df['EMA7'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 7, fillna= False).ema_indicator()
    df['EMA14'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 14, fillna= False).ema_indicator()
    df['EMA50'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 50, fillna= False).ema_indicator()
    df['EMA200'] = ta.trend.EMAIndicator(close= df['Adj Close'], window= 50, fillna= False).ema_indicator()
    df['MACD_line'] =ta.trend.MACD(close= df['Adj Close'], window_slow= 26, window_fast= 12, window_sign = 9, fillna = False).macd()
    df['MACD_signal'] =ta.trend.MACD(close= df['Adj Close'], window_slow= 26, window_fast= 12, window_sign = 9, fillna = False).macd_signal()
    df['MACD_diff'] =ta.trend.MACD(close= df['Adj Close'], window_slow= 26, window_fast= 12, window_sign = 9, fillna = False).macd_diff()
    df['RSI']=ta.momentum.RSIIndicator(close= df['Adj Close'], window= 14, fillna= False).rsi()
    df['OBV']= ta.volume.OnBalanceVolumeIndicator(close= df['Adj Close'], volume= df['Volume'], fillna = False).on_balance_volume()
    df['BBH']=ta.volatility.BollingerBands(df['Adj Close'], window = 20, window_dev = 2, fillna = False).bollinger_hband_indicator()
    df['BBL']=ta.volatility.BollingerBands(df['Adj Close'], window = 20, window_dev = 2, fillna = False).bollinger_lband_indicator()
    df=df.dropna()
    df = df.drop(columns=['Open', 'High', 'Low', 'Close', 'Volume'],axis=1)
    
    return df


# loading individual stock data for every stock and calling add_indicator

df_GOOG = yf.download(tickers = 'GOOG' ,period = "2y", interval = "1h",ignore_tz = True,prepost = False) 
df_GOOG = add_indicators(df_GOOG)

df_IBM = yf.download(tickers = 'IBM' ,period = "2y", interval = "1h",ignore_tz = True,prepost = False) 
df_IBM = add_indicators(df_IBM)

df_AAPL = yf.download(tickers = 'AAPL' ,period = "2y", interval = "1h",ignore_tz = True,prepost = False) 
df_AAPL = add_indicators(df_AAPL)

df_META = yf.download(tickers = 'META' ,period = "2y", interval = "1h",ignore_tz = True,prepost = False) 
df_META = add_indicators(df_META)

df_AMZN = yf.download(tickers = 'AMZN' ,period = "2y", interval = "1h",ignore_tz = True,prepost = False) 
df_AMZN = add_indicators(df_AMZN)

train_data1 = df_GOOG.iloc[:-split_line,:]
train_data2 = df_IBM.iloc[:-split_line,:]
train_data3 = df_AAPL.iloc[:-split_line,:]
train_data4 = df_META.iloc[:-split_line,:]
train_data5 = df_AMZN.iloc[:-split_line,:]

train_df_list = [train_data1,train_data2,train_data3,train_data4,train_data5]

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[*********************100%%**********************]  1 of 1 completed
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[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed


import yfinance as yf

tickers = ['GOOG', 'IBM', 'AAPL', 'META', 'AMZN']
train_df_list = []

for ticker in tickers:
    df = yf.download(tickers=ticker, period="2y", interval="1h", ignore_tz=True, prepost=False)
    df = add_indicators(df)
    train_data = df.iloc[:-split_line, :]
    train_df_list.append(train_data)

[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed


'''
training loop to handle multiple dataframes like df_1, df_2, df_3, you can create a list of dataframes and loop through them. 
'''
# Load the stock dataframes
data_list = train_df_list

# Create and train the agent
agent = DQNAgent(state_size, env.action_space, memory_size=4000) # increasing the memory size for the agent 
episodes = 100
batch_size = 32

for data in data_list:
    env = StockTradingEnv(data)
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.nvec.prod()

    for e in tqdm(range(episodes)):
        state = env.reset()
        state = np.reshape(state, [1, state_size])
        total_reward = 0

        while True:
            action = agent.act(state)
            next_state, reward, done, _ = env.step(action)
            total_reward += reward
            next_state = np.reshape(next_state, [1, state_size])

            agent.remember(state, action, reward, next_state, done)
            state = next_state

            if done:
                # print(f'Episode: {e + 1}/{episodes}, Total Reward: {total_reward}, Epsilon: {agent.epsilon:.2}')
                break

        if len(agent.memory) > batch_size:
            agent.replay(batch_size)

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
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# using Apple data for testing
test_data = df_AAPL.iloc[-split_line:,:]

# Create the test environment
test_env = StockTradingEnv(test_data,initial_cash =10000)

# Define the number of Monte Carlo runs
monte_carlo_runs = 200
rewards = []

for run in tqdm(range(monte_carlo_runs)):
    # Test the agent
    state = test_env.reset()
    state = np.reshape(state, [1, state_size])
    total_reward = 0

    while True:
        action = agent.act(state)
        next_state, reward, done, _ = test_env.step(action)
        total_reward += reward
        next_state = np.reshape(next_state, [1, state_size])
        state = next_state

        if done:
            # print(f'Test Run: {run + 1}/{monte_carlo_runs}, Total Reward: {total_reward}')
            rewards.append(total_reward)
            break

# Calculate and print the average reward
average_reward = np.mean(rewards)
print(f'Average Reward after {monte_carlo_runs} runs: {average_reward}')

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  0%|▍                                                                                 | 1/200 [00:09<31:59,  9.64s/it]

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  1%|▊                                                                                 | 2/200 [00:20<34:57, 10.59s/it]

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  2%|█▏                                                                                | 3/200 [00:30<33:47, 10.29s/it]

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  2%|█▋                                                                                | 4/200 [00:42<34:52, 10.68s/it]

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	Open	High	Low	Close	Adj Close	Volume
Datetime
2022-02-14 09:30:00	4412.609863	4423.459961	4388.700195	4401.009766	4401.009766	0
2022-02-14 10:30:00	4400.910156	4412.129883	4385.520020	4409.120117	4409.120117	306495400
2022-02-14 11:30:00	4409.160156	4419.509766	4403.939941	4419.470215	4419.470215	218798688
2022-02-14 12:30:00	4419.709961	4426.220215	4408.850098	4410.520020	4410.520020	184727000
2022-02-14 13:30:00	4410.560059	4410.810059	4364.839844	4387.220215	4387.220215	296196000

	Adj Close	EMA7	EMA14	EMA50	EMA200	MACD_line	MACD_signal	MACD_diff	RSI	OBV	BBH	BBL
Datetime
2022-02-24 09:30:00	4162.600098	4237.179196	4271.523288	4346.750940	4346.750940	-45.858670	-35.851188	-10.007482	22.713345	-3147033706	0.0	1.0
2022-02-24 10:30:00	4184.149902	4223.921872	4259.873503	4340.374429	4340.374429	-48.764437	-38.433838	-10.330599	28.374963	-2700450575	0.0	0.0
2022-02-24 11:30:00	4171.790039	4210.888914	4248.129041	4333.763276	4333.763276	-51.471287	-41.041328	-10.429959	27.146655	-3034007575	0.0	0.0
2022-02-24 12:30:00	4173.439941	4201.526671	4238.170495	4327.476087	4327.476087	-52.873855	-43.407833	-9.466022	27.597219	-2741888575	0.0	0.0
2022-02-24 13:30:00	4214.890137	4204.867537	4235.066447	4323.060951	4323.060951	-50.063613	-44.738989	-5.324624	37.975458	-2387069575	0.0	0.0

Price	Adj Close					Close					...	Open					Volume
Ticker	AAPL	AMZN	GOOG	IBM	META	AAPL	AMZN	GOOG	IBM	META	...	AAPL	AMZN	GOOG	IBM	META	AAPL	AMZN	GOOG	IBM	META
Datetime
2022-02-14 09:30:00	168.419998	155.217499	135.050003	130.197403	218.000000	168.419998	155.217499	135.050003	130.197403	218.000000	...	168.059998	151.751007	133.365509	132.589996	219.309998	18117555	837927	334598	981373	9233434
2022-02-14 10:30:00	168.990005	157.978241	135.990997	129.389999	218.360001	168.990005	157.978241	135.990997	129.389999	218.360001	...	168.429901	155.250000	135.199997	130.190002	218.029999	9003239	635340	199893	709559	5181970
2022-02-14 11:30:00	169.330002	158.012497	135.879501	130.132507	218.848007	169.330002	158.012497	135.879501	130.132507	218.848007	...	169.000000	158.050003	136.031494	129.380005	218.369995	7083897	468100	119565	741341	3520821
2022-02-14 12:30:00	168.959000	157.000000	135.298004	130.500000	217.240005	168.959000	157.000000	135.298004	130.500000	217.240005	...	169.315002	157.975006	136.000000	130.132507	218.830002	5098138	336430	114256	501309	3902549
2022-02-14 13:30:00	167.500000	155.664505	134.761993	129.470001	216.344101	167.500000	155.664505	134.761993	129.470001	216.344101	...	168.964996	156.993500	135.356491	130.509995	217.199997	9465749	536293	130340	523215	6045084

Reinforcement Learning in Trading¶

Table of Contents¶

Setup¶

Installing Required Libraries¶

Importing Required Libraries¶

Forward Planning:¶

Loading the Data¶

Step 1: Data Preparation (Adding Trading Indicators)¶

Trend Indicators¶

EMAs : Exponential moving averages¶

Momentum Indicators¶

RSI: Relative Strength Index¶

Volume Indicators¶

Volatility Indicators¶

Step 2: Define the environment¶

Step 3: Define The Agent¶

Step 4: Train the Agent¶

Step 5: Test the agent¶

Monte Carlo for Testing¶

Step 6: Refine the model¶

Practice: Adding More Data for Training¶

plotting the Adj Close for the 5 companies¶

Let's create a function that adds all the indicators to the dataframe¶

Exercise: optimize the above code¶

Training the Model with More Data¶

Testing the Data¶

Final Reflection, comments and mentions¶